Reliable Monte Carlo Localization for Mobile Robots

Reliability is a key factor for realizing safety guarantee of full autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick re-localization, simultaneously. The presented method can be implemented using similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables us to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick re-localization from failures can be realized while mitigating noisy influence of global localization. Through three types of experiments, we show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized

Stein Variational Guided Model Predictive Path Integral Control: Proposal and Experiments with Fast Maneuvering Vehicles

This paper presents a novel Stochastic Optimal Control (SOC) method based on Model Predictive Path Integral control (MPPI), named Stein Variational Guided MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action distributions. While MPPI can find a Gaussian-approximated optimal action distribution in closed form, i.e., without iterative solution updates, it struggles with multimodality of the optimal distributions, such as those involving non-convex constraints for obstacle avoidance. This is due to the less representative nature of the Gaussian. To overcome this limitation, our method aims to identify a target mode of the optimal distribution and guide the solution to converge to fit it. In the proposed method, the target mode is roughly estimated using a modified Stein Variational Gradient Descent (SVGD) method and embedded into the MPPI algorithm to find a closed-form "mode-seeking" solution that covers only the target mode, thus preserving the fast convergence property of MPPI. Our simulation and real-world experimental results demonstrate that SVG-MPPI outperforms both the original MPPI and other state-of-the-art sampling-based SOC algorithms in terms of path-tracking and obstacle-avoidance capabilities. Source code: https://github.com/kohonda/proj-svg_mppiComment: 7 pages, 5 figure

成人発症の微小変化型ネフローゼ症候群に対するプレドニゾロン初期投与量と，寛解，再発，及び感染症との関連

Background: A dose of 0.5-1 mg/kg/day of prednisolone (PSL) is administered for the initial treatment of minimal change disease (MCD). However, little is known about the optimal PSL dose for the initial treatment of MCD. Methods: We conducted a retrospective multicenter cohort study of treatment-naive adult patients with MCD diagnosed by renal biopsy from 1981 to 2015 in whom PSL monotherapy was performed as the initial treatment. The exposure of interest was an initial median PSL dose of < 0.63 mg/kg/day (Group L) compared to ≥ 0.63 mg/kg/day (Group H). Cumulative remission and relapse after remission were compared between these groups using Cox regression adjusted for baseline characteristics. Results: Ninety-one patients met the inclusion criteria. During a median follow-up of 2.98 years, 87 (95.6%) patients achieved complete remission, and 47.1% relapsed after remission. There was no significant difference in the remission rate between the groups at 4 weeks of follow-up (66.7 vs. 82.6%). The median time to remission in Group L was comparable to that in Group H (17.0 vs. 14.0 days). A multivariable Cox hazard model revealed that the initial PSL dose was not a significant predictor of remission. The cumulative steroid doses at 6 months, 1 year, and 2 years after treatment initiation were significantly lower in Group L than in Group H. Conclusion: The initial PSL dose was not associated with time to remission, remission rate, time to relapse, or relapse rate. Therefore, a low initial steroid dose may be sufficient to achieve remission.博士（医学）・甲第803号・令和3年12月21日© 2021. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Synthetic Study of dl-Tetrodotoxin from myo-Inositol (Part 2)

講演番号：3 G3 37 日本化学会第72春季年会, 平成9年3月27日～3月30日, 東

Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content

Rare-earth elements like neodymium, terbium and dysprosium are crucial to the performance of permanent magnets used in various green-energy technologies like hybrid or electric cars. To address the supply risk of those elements, we applied machine-learning techniques to design magnetic materials with reduced neodymium content and without terbium and dysprosium. However, the performance of the magnet intended to be used in electric motors should be preserved. We developed machine-learning methods that assist materials design by integrating physical models to bridge the gap between length scales, from atomistic to the micrometer-sized granular microstructure of neodymium-iron-boron permanent magnets. Through data assimilation, we combined data from experiments and simulations to build machine-learning models which we used to optimize the chemical composition and the microstructure of the magnet. We applied techniques that help to understand and interpret the results of machine learning predictions. The variables importance shows how the main design variables influence the magnetic properties. High-throughput measurements on compositionally graded sputtered films are a systematic way to generate data for machine data analysis. Using the machine learning models we show how high-performance, Nd-lean magnets can be realized

Numerical Simulation of Spray Combustion with Ultrafine Oxygen Bubbles

In this study, we focused on a fuel reforming technology by applying ultrafine oxygen bubble as the pretreatment for in-cylinder combustion s. It is assumed that oxygen is dissolved in the droplets in the form of ultrafine bubbles, and released into air when the decane fuel evaporates. A numerical simulation of the spray combustion was conducted using a PSI-CELL model. We changed the oxygen concentration of the droplets, the initial droplet diameter, and the number of injected droplets per unit time to discuss the ignition time and the temperature field. When there is no oxygen in the fuel droplet, most of the flames are diffusion flames. On the other hand, when oxygen exists in the droplets, premixed flames are formed at the upstream edge of the fuel spray. Due to the effects of ultrafine oxygen bubbles, the ignition time is shortened. However, on the condition that there is only a small amount of oxygen in the fuel droplets, as more fuel is supplied by enlarging the droplet diameter or increasing the number of injected droplets per unit time, the ignition time increases. Thus, when discussing ignition time, the balance between evaporated fuel and oxygen in the gas phase is important

Numerical Simulation of Spray Combustion with Ultrafine Oxygen Bubbles

In this study, we focused on a fuel reforming technology by applying ultrafine oxygen bubble as the pretreatment for in-cylinder combustion s. It is assumed that oxygen is dissolved in the droplets in the form of ultrafine bubbles, and released into air when the decane fuel evaporates. A numerical simulation of the spray combustion was conducted using a PSI-CELL model. We changed the oxygen concentration of the droplets, the initial droplet diameter, and the number of injected droplets per unit time to discuss the ignition time and the temperature field. When there is no oxygen in the fuel droplet, most of the flames are diffusion flames. On the other hand, when oxygen exists in the droplets, premixed flames are formed at the upstream edge of the fuel spray. Due to the effects of ultrafine oxygen bubbles, the ignition time is shortened. However, on the condition that there is only a small amount of oxygen in the fuel droplets, as more fuel is supplied by enlarging the droplet diameter or increasing the number of injected droplets per unit time, the ignition time increases. Thus, when discussing ignition time, the balance between evaporated fuel and oxygen in the gas phase is important